Machine Learning with Scikit-Learn in 7 Hours

Machine learning is the buzzword bringing computer science and statistics together to build smart and efficient models. scikit–learn has evolved as a robust library for machine learning applications in Python with support for a wide range of supervised and unsupervised learning algorithms. If you’re a data scientist or an IT professional who wants to learn machine learning using Python’s popular machine learning library scikit–learn, then go for this course.

This course teaches you how to perform your day–to–day machine learning tasks with scikit–learn. It’s a perfect blend of concepts and practical examples which makes it easy to understand and implement. You will begin with learning important machine learning algorithms that are commonly used in the field of data science such as Linear regression, Logistic Regression, SVM, Naive Bayes, K–Means, Random Forest, and Feature engineering. You will also be taken through supervised learning, unsupervised learning, reinforcement learning, and semi–supervised learning with hands–on practical projects.

Contents and Overview

This training program includes 3 complete courses, carefully chosen to give you the most comprehensive training possible.

In the first course, Fundamentals of Machine Learning with scikit–learn, you will learn all the important machine learning algorithms that are commonly used in the field of data science. These algorithms can be used for supervised as well as unsupervised learning, reinforcement learning, and semi–supervised learning. The algorithms you will be learning are Linear regression, Logistic Regression, SVM, Naive Bayes, K–Means, Random Forest, and Feature engineering.